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1 This presentation is for informational purposes only and may not be incorporated into a contract or agreement.

2 Supporting Keyword Columns with Ontology-based Referential Constraints Eugene Inseok Chong, Souripriya Das, George Eadon, Jagannathan Srinivasan Oracle

3 Outline Introduction Problem Description Our Approach Implementation Scheme Summary Conclusions

4 Introduction

5 Keyword Columns Keyword Column refers to a column that holds one or more keywords. Example Diagnosis Table patient_id diagnosis Rh. Arthritis AIDS AIDS Keyword Column

6 Keyword Columns Keyword columns characterize table rows The keywords referenced typically belong to a knowledge domain (ontology). However, the semantic link to the corresponding knowledge domain is currently NOT captured in database systems.

7 Example: Semantically linking keyword column to an Ontology Cancer_ontology Diagnosis Table Diseases_and_Disorders patient_id 1721 Diagnosis Rh. Arthritis Immune_System_Disorder AIDS AIDS AI_Disease ID_Syndrome Rh. Arthritis T-Cell_ID AIDS

8 Key Benefits of Semantic Link Enhances data integrity for keyword columns Facilitates evolution of keyword columns with ontology Facilitates semantic search on keyword columns

9 Ontology-based Semantic Match Diagnosis Table Cancer_ontology patient_id 1721 Diagnosis Rh. Arthritis Diseases_and_Disorders AIDS AIDS Syntactic Match: Diagnosis = Immune_System_Disorder Semantic Match: Diagnosis rdfs:subclassof Immune_System_Disorder Immune_System_Disorder AI_Disease ID_Syndrome Rh. Arthritis T-Cell_ID AIDS

10 Approach: Extend Database Referential Constraints Ontology-based constraint definition Query on Ontology identifies keyword domain On Ontology Update Semantics On Deletion transformation of keywords On Insertion refinement of keywords Enforcing Ontology-based Constraint Uses materialized view (MV), conventional referential integrity constraint, and triggers

11 Key Contributions Ontology-based Referential Constraints Automatic evolution of keywords with Ontology An efficient constraint enforcement scheme

12 Related work Broad Areas Data Integrity, RDF [5], RDFS [6], OWL [7], RDF Querying [15, 11], OWL Querying [10], OWL Ontology Editing [8] Specifics Conventional Referential Constraints [14] Our work extends this for keyword columns Ontology aligning and Merging [12, 13] Automatic evolution of keywords columns with ontology is somewhat similar.

13 Problem Description

14 Background: RDF_MATCH Table Function Select those compounds that are subclasses of proteins and also subclasses of complexes using BioCyc ontology and RDFS inferencing. Select t.compound from TABLE( RDF_MATCH ( (?compound rdfs:subclassof :Proteins) (?compound rdfs:subclassof :Complexes), RDFModels( BioCyc ), RDFRules( rdfs ), null)) t;

15 Constraining Keyword Columns Constrained keyword column (K) Referenced ontology (O) Constraint query (Q) Constraint query result set (R)

16 Components of Ontology-based Referential Constraint Constraint Query (Q) Select t.d from Table (RDF_MATCH( )) t; Referenced Ontology (O) Diseases_and_Disorders Diagnosis Table Constraint Query Result Set (R) {Rh. Arthritis, AIDS, T-Cell_ID, } patient_id Diagnosis Keyword column (K) Rh. Arthritis AIDS AIDS Immune_System_Disorder AI_Disease ID_Syndrome Rh. Arthritis T-Cell_ID AIDS Cancer_ontology

17 Evolution of Keywords R-terms are O-related, not isolated Ontology specifies relationships between terms in the constraint query result set As ontology evolves, utilize the relationships to do (or suggest) replacements or refinements using Transformation function (f t ) Refinement function (f r )

18 Transformation function ON DELETE (from R) TRANSFORM using f t f t : <O old, R new, deletedterm> termset f t functionality Locate deleted term in old (pre-change) ontology Use custom algorithm to compute the (possibly singleton) set of possible replacements The termset must be a subset of new result set Use returned termset to Replace with the element, if singleton Replace with NULL and add <rowid, deletedterm, termset> to recommendations table

19 Refinement function ON INSERT (into R) recommend using f r f r : <O new, R new, insertedterm> termset f r functionality Locate inserted term in the modified ontology Use custom algorithm to compute the subset of terms in new result set R new that it could refine Add to recommendations

20 Our Approach

21 Referential Constraint using Constraint Query Traditional database referential constraint <col a> REFERENCES <table t2> (<col b>) Ontology-based referential constraint <col a> REFERENCES (<constraint query>) Example: chemical_compound REFERENCES SELECT t.compound FROM TABLE( RDF_MATCH ( (?compound rdfs:subclassof :Proteins) (?compound rdfs:subclassof :Complexes), RDFModels( BioCyc ), RDFRules( rdfs ), null)) t;

22 On Ontology Update Modifications made to ontology O may cause No change in R Deletion of a term in R not currently used in K Deletion of a term in R that is used in K Addition of a term in R Renaming of a term in R not used in K Renaming of a term in R that is used in K

23 Example: Ontology Update Referenced Ontology Signs and Synptoms Sensation Hearing Smell Body Temperature Changes Pain Taste Hypothermia Fever

24 ON DELETE from result set Syntax <col> REFERENCES <constraint query> ON DELETE TRANSFORM USING <function> Example Deletion of Hypothermia causes transformation to Body Temperature Changes Deletion of Pain generates recommendations Recommendations Table Rowid Term Recommendations Action R105 Pain Sensation Signs and Symptoms Delete

25 ON INSERT into result set Syntax ON INSERT RECOMMEND USING <function> Example If new terms Hearing, Smell, and Taste are inserted under Sensation and get added to R then appropriate recommendation gets generated for possible refinement Rowid Term Recommendations Table Recommendations Action NULL Sensation Hearing Smell Taste Insert

26 Implementation Scheme

27 System Architecture Constraint Definition Module Constraint Enforcement Module Ontology Editor Core DBMS Ontology Storage & Querying User Tables and Ontologies

28 Overview Use a Materialized View (MV) to store constrained query result set Define conventional database referential constraint from keyword column to MV This restricts illegal update or insert of keyword values Upon ontology modification Do batch or incremental maintenance (refresh) of MV, and Use triggers to transform keyword data and/or generate recommendations

29 Batch Enforcement DISABLE constraint Save MV data containing current result set R Save a copy of current version of ontology O ENABLE constraint Compare new MV (result set R new ) with old MV (result set R old ) to determine the deletions and additions Handle deletions and additions, with appropriate transformations and/or recommendations Finally, enable constraint on MV, and provide EXCEPTIONS table for storing any exceptions

30 Incremental Enforcement Materialized View (MV) defined using Constraint Query and stores Constraint Query Result Set Referential Constraint: ON DELETE SET NULL Keyword column MV def. on Ontology Row-level Triggers for any changes Journal Table for MV stores any changes to MV After Refresh processing Invoke transformation and refinement functions (as needed) to update keyword column values and update entries in recommendation table Referenced Ontology Row-level update Trigger for SET TO NULL Recommendation Table

31 Incr. Enf.: AFTER REFRESH triggers Materialized View (MV) defined using Constraint Query and stores Constraint Query Result Set Referential Constraint Keyword column MV def. on Ontology Row-level Triggers for any changes Journal Table for MV stores any changes to MV After Refresh processing Invoke transformation and refinement functions (as needed) to update keyword column values and insert in recommendation table Referenced Ontology Recommendation Table

32 Summary Ontology-based referential constraints for keyword columns: Uses constraint query on ontology to identify domain for keyword column Transformation and refinement functions for handling deletion of referenced terms and addition of new terms to the domain, respectively. Implementation scheme discussed how to support this functionality using Oracle s MVs and triggers. Also proposed some extensions to MV support.

33 Conclusions Supporting ontology-based referential constraints Enhances data integrity of keyword columns Facilitates keeping up with changes in ontology that affects domain

34 Q U E S T I O N S A N S W E R S

35

36 Background: Overview Ontology, Entailment, Mapping ontology RDF, RDF Schema, OWL SPARQL ONT_EXPAND and RDF_MATCH table function ONT_RELATED operator

37 Background: ONT_RELATED semantic match operator Select m.article_id from medical_articles m Where ONT_RELATED ( m.keyword, rdfs:subclassof, Signs and Symptoms, MeSH ) m;

38 Background: ONT_EXPAND and RDF_MATCH Table Functions Select term1name from TABLE(ONT_EXPAND( NULL, rdfs:subclassof, Signs and Symptoms, MeSH ) Select t.compound from TABLE( RDF_MATCH ( (?compound rdfs:subclassof :Proteins) (?compound rdfs:subclassof :Complexes), RDFModels( BioCyc ), RDFRules( rdfs ), null)) t;

39 Evolution of Keywords Referenced ontology (O) contains information about relationships between the terms in constraint query result set (R) This knowledge provides scope for automatic evolution of the content of the keyword column (K) when change to O and/or to the constraint query (Q) causes changes in R. Values in keyword column K can be transformed automatically or recommendations may be generated for possible replacements or refinements

40 Batch Refinement function Batch refinement function (f batch_refine ) On a batch of changes to ontology O, some of which may not affect R, recommend using f br f br : <O current, R current, valueset> refineset f br functionality Use custom algorithm to analyze the ontology to see if recommendations for refinements may be generated

41 Querying Keyword Columns Operand2 Operand1 No associated ontology Ontology O1 No associated ontology Use ontology in query Use ontology O 1 Ontology O 2 Use Ontology O 2 If O 1 =O 2, use that; Otherwise, use O 1, O 2, and a mapping ontology

42 Bulk modification Handling For any bulk-modification operation affecting the referenced ontology or the user table DISABLE the constraint before starting ENABLE the constraint at the end, providing EXCEPTIONS table for recording the exceptions Bulk-load into the ontology may result in new recommendations for refinement Bulk-load into user table may generate exceptions in the EXCEPTIONS table

43 Incremental Enforcement MV (i.e., result set) is incrementally refreshed ON COMMIT Alter the conventional referential constraint between the MV and the constrained column to be deferred till commit time when ON DELETE SET NULL will set to NULL any value deleted from MV. Row-level triggers on the MV journals any changes to the MV to a temporary journal table Row-level update trigger on keyword column writes <rowid, keyword,, delete > into recommendations table when ON DELETE SET NULL replaces a keyword value with NULL. After commit (and after MV has been refreshed), we process the journal table by removing series of change operations that are idempotent and then invoking the transformation and refinement functions based on the remaining operations.

44 Improved Incremental Enforcement Requires support for AFTER REFRESH and AFTER REFRESH ERROR triggers on MV Any modification to MV during REFRESH is journaled to a temporary journal table An AFTER REFRESH trigger will call the transformation and refinement functions as needed to update the keyword column and the recommendations table, based upon the changes to MV as recorded in the journal table

45 Improved Incremental Enforcement AFTER REFRESH ERROR trigger is useful in catching errors during REFRESH and may be used to return helpful errors. Avoids following problems No unnecessary NULL setting of keyword values whose disappearance is only transient during REFRESH. This also avoids use of row update trigger on keyword column. Processing of temporary journal table happens during the transaction that modifies the ontology, thereby avoiding any non-atomicity issues Errors encountered during MV REFRESH gets reported directly to the transaction that updated the ontology.